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import streamlit as st
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import logging
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from .semantic_process import process_semantic_analysis
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from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input
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from ..database.database_oldFromV2 import store_file_semantic_contents, retrieve_file_contents, delete_file, get_user_files
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from ..utils.widget_utils import generate_unique_key
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from .semantic_float_reset import semantic_float_init, float_graph, toggle_float_visibility, update_float_content
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logger = logging.getLogger(__name__)
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semantic_float_init()
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def get_translation(t, key, default):
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return t.get(key, default)
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def display_semantic_interface(lang_code, nlp_models, t):
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if 'semantic_chatbot' not in st.session_state:
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st.session_state.semantic_chatbot = initialize_chatbot('semantic')
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if 'semantic_chat_history' not in st.session_state:
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st.session_state.semantic_chat_history = []
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st.markdown("""
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<style>
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.stTabs [data-baseweb="tab-list"] { gap: 24px; }
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.stTabs [data-baseweb="tab"] {
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height: 50px;
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white-space: pre-wrap;
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background-color: #F0F2F6;
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border-radius: 4px 4px 0px 0px;
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gap: 1px;
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padding-top: 10px;
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padding-bottom: 10px;
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}
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.stTabs [aria-selected="true"] { background-color: #FFFFFF; }
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.file-list {
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border: 1px solid #ddd;
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border-radius: 5px;
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padding: 10px;
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margin-top: 20px;
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}
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.file-item {
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display: flex;
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justify-content: space-between;
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align-items: center;
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padding: 5px 0;
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border-bottom: 1px solid #eee;
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}
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.file-item:last-child { border-bottom: none; }
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.chat-container {
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height: 400px;
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overflow-y: auto;
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border: 1px solid #ddd;
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padding: 10px;
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margin-bottom: 10px;
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}
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.chat-input { border-top: 1px solid #ddd; padding-top: 10px; }
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.stButton { margin-top: 0 !important; }
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.graph-container {
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border: 1px solid #ddd;
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border-radius: 5px;
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padding: 10px;
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height: 500px;
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overflow-y: auto;
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}
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</style>
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""", unsafe_allow_html=True)
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st.markdown(f"<div class='semantic-initial-message'>{t['semantic_initial_message']}</div>", unsafe_allow_html=True)
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tab1, tab2 = st.tabs(["Upload", "Analyze"])
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with tab1:
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st.subheader("File Management")
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uploaded_file = st.file_uploader("Choose a file to upload", type=['txt', 'pdf', 'docx', 'doc', 'odt'], key=generate_unique_key('semantic', 'file_uploader'))
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if uploaded_file is not None:
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file_contents = uploaded_file.getvalue().decode('utf-8')
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if store_file_semantic_contents(st.session_state.username, uploaded_file.name, file_contents):
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st.success(f"File {uploaded_file.name} uploaded and saved successfully")
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else:
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st.error("Error uploading file")
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st.markdown("---")
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st.subheader("Manage Uploaded Files")
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user_files = get_user_files(st.session_state.username, 'semantic')
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if user_files:
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for file in user_files:
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col1, col2 = st.columns([3, 1])
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with col1:
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st.write(file['file_name'])
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with col2:
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if st.button("Delete", key=f"delete_{file['file_name']}", help=f"Delete {file['file_name']}"):
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if delete_file(st.session_state.username, file['file_name'], 'semantic'):
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st.success(f"File {file['file_name']} deleted successfully")
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st.rerun()
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else:
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st.error(f"Error deleting file {file['file_name']}")
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else:
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st.info("No files uploaded yet.")
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with tab2:
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st.subheader("Semantic Analysis")
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col_left, col_right = st.columns([3, 2])
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with col_left:
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st.subheader("File Selection and Analysis")
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user_files = get_user_files(st.session_state.username, 'semantic')
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file_options = [get_translation(t, 'select_saved_file', 'Select a saved file')] + [file['file_name'] for file in user_files]
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selected_file = st.selectbox("", options=file_options, key=generate_unique_key('semantic', 'file_selector'))
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if st.button("Analyze Document"):
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if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'):
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file_contents = retrieve_file_contents(st.session_state.username, selected_file, 'semantic')
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if file_contents:
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with st.spinner("Analyzing..."):
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try:
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nlp_model = nlp_models[lang_code]
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concept_graph, entity_graph, key_concepts = process_semantic_analysis(file_contents, nlp_model, lang_code)
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st.session_state.concept_graph = concept_graph
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st.session_state.entity_graph = entity_graph
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st.session_state.key_concepts = key_concepts
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st.session_state.current_file_contents = file_contents
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st.success("Analysis completed successfully")
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if 'graph_id' not in st.session_state:
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st.session_state.graph_id = float_graph(
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content="<div id='semantic-graph'>Loading graph...</div>",
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width="40%",
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height="60%",
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position="bottom-right",
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shadow=2,
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transition=1
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)
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update_float_content(st.session_state.graph_id, f"""
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<h3>Key Concepts:</h3>
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<p>{', '.join([f"{concept}: {freq:.2f}" for concept, freq in key_concepts])}</p>
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<img src="data:image/png;base64,{concept_graph}" alt="Concept Graph" style="width:100%"/>
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""")
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st.session_state.graph_visible = True
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except Exception as e:
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logger.error(f"Error during analysis: {str(e)}")
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st.error(f"Error during analysis: {str(e)}")
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st.session_state.concept_graph = None
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st.session_state.entity_graph = None
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st.session_state.key_concepts = []
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else:
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st.error("Error loading file contents")
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else:
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st.error("Please select a file to analyze")
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st.subheader("Chat with AI")
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chat_container = st.container()
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with chat_container:
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st.markdown('<div class="chat-container">', unsafe_allow_html=True)
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for message in st.session_state.semantic_chat_history:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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st.markdown('</div>', unsafe_allow_html=True)
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user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
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col1, col2 = st.columns([3, 1])
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with col1:
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send_button = st.button("Send", key=generate_unique_key('semantic', 'send_message'))
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with col2:
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clear_button = st.button("Clear Chat and Graph", key=generate_unique_key('semantic', 'clear_chat'))
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if send_button and user_input:
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st.session_state.semantic_chat_history.append({"role": "user", "content": user_input})
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if user_input.startswith('/analyze_current'):
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response = process_semantic_chat_input(user_input, lang_code, nlp_models[lang_code], st.session_state.get('current_file_contents', ''))
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else:
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response = st.session_state.semantic_chatbot.generate_response(user_input, lang_code, context=st.session_state.get('current_file_contents', ''))
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st.session_state.semantic_chat_history.append({"role": "assistant", "content": response})
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st.rerun()
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if clear_button:
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if st.session_state.semantic_chat_history:
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if st.button("Do you want to export the analysis before clearing?"):
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st.success("Analysis exported successfully")
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st.session_state.semantic_chat_history = []
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if 'graph_id' in st.session_state:
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toggle_float_visibility(st.session_state.graph_id, False)
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del st.session_state.graph_id
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st.session_state.concept_graph = None
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st.session_state.entity_graph = None
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st.session_state.key_concepts = []
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st.rerun()
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with col_right:
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st.subheader("Visualization")
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if 'key_concepts' in st.session_state and st.session_state.key_concepts:
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st.write("Key Concepts:")
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st.write(', '.join([f"{concept}: {freq:.2f}" for concept, freq in st.session_state.key_concepts]))
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tab_concept, tab_entity = st.tabs(["Concept Graph", "Entity Graph"])
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with tab_concept:
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if 'concept_graph' in st.session_state and st.session_state.concept_graph:
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st.image(st.session_state.concept_graph)
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else:
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st.info("No concept graph available. Please analyze a document first.")
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with tab_entity:
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if 'entity_graph' in st.session_state and st.session_state.entity_graph:
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st.image(st.session_state.entity_graph)
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else:
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st.info("No entity graph available. Please analyze a document first.") |
